Abstract

1. Introduction

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, and it is characterized by progressive decline of cognitive function and memory formation in mostly elderly populations (Minati et al., 2009). While the exact pathophysiology of AD has yet to be determined, it is known that the greatest risk factor for development of the disease is age. AD presents with a very characteristic neuropathology: intraneuronal accumulations of hyperphosphorylated microtuble-associated protein tau known as neurofibrillary tangles (NFTs), and extracellular deposition of amyloid β-peptide (Aβ) known as amyloid or senile plaques (Dickson et al., 1988; Alzheimer et al., 1995). AD patients also exhibit a significant loss of synapses and neurons, namely in the temporal and frontal cortices and hippocampi (Davies et al., 1987; Scheff et al., 2006; Scheff et al., 2007). Increasing evidence suggests that it is the aggregation of Aβ fragments that causes neurotoxic effects that lead to the neural, synaptic, and cognitive degradation seen in AD, making Aβ the target of most AD research today (Hardy and Selkoe, 2002).

2. APP and Aβ biology

Aβ is formed by the proteolytic cleavage of amyloid precursor protein (APP) (Zhao et al.), a protein that contains an Aβ-encoding region. As APP is secreted from the cell, it undergoes two consecutive proteolytic cleavages by a series of secretases: α then γ, or β then γ (Esch et al., 1990). Members of the ADAM (a disintegrin and metalloproteinase) family have α-secretase activity and are implicated in APP cleavage (Zhang et al., 2011), but because α-secretase cleaves within the Aβ encoding region, it is not a factor in Aβ formation. β-site APP converting enzyme (BACE) 1 and 2 are β-secretases, and both have been implicated as major factors in the formation of Aβ (Vassar et al., 1999). The γ-secretase complex consists of four components: presenilin 1 or 2 (PS1, PS2, the catalytic domains), Nicastrin, APH-1, and presenilin enhancer-2 (PEN-2) (Kimberly et al., 2003; Takasugi et al., 2003). Missense mutations within the PS1 and PS2 genes are the most common cause of early onset familial AD (Levy-Lahad et al., 1995; Sherrington et al., 1995), but mutations in APP have also been seen in more elderly familial cases (Citron et al., 1992). In addition to cleaving APP, γ-secretase also cleaves a large number of transmembrane proteins, including Notch (Haass and De Strooper, 1999), whose cleaved intracellular domain plays an important role as a transcriptional factor in regulating genes involved in development (Kopan et al., 1996; Schroeter et al., 1998).

For non-amyloidogenic cleavage of APP, α-secretase cleaves the protein between amino acid residues 16 and 17 within the Aβ region, creating a short 83-residue fragment known as p83 (Esch, et al. 1990). The p83 fragment is subsequently cleaved by γ-secretase within the transmembrane region, generating p3 (Aβ17-40) and APP intracellular domain (AICD). AICD can potentially translocate to the nucleus and function as a transcription factor Cao and Sudhof (2001); (Baek et al., 2002; Leissring et al., 2002; Cao and Sudhof, 2004). The Aβ fragment is created by cleavage with β- then γ-secretases (Seubert et al., 1993). β-secretase cleaves at the N-terminus of the Aβ region and is followed by cleavage by γ-secretase to create a 40-42-residue Aβ, with the majority of Aβ fragments being Aβ40, and the less common, more amyloidogenic species being Aβ42. Studies of Aβ deposition in Down Syndrome and biochemical studies of both Aβ40 and Aβ42 have shown that the Aβ42 fragment tends to aggregate into fibrils more readily than the Aβ40 fragment, causing a gradual increase of Aβ42 deposition with age (Jarrett et al., 1993; Iwatsubo et al., 1995; Lemere et al., 1996).

A large number of studies have proven that Aβ has a neurotoxic effect when aggregated in specific forms (Lambert et al., 1998; Hardy and Selkoe, 2002; Walsh et al., 2002). Its formation and build-up precedes the accumulation of hyperphosphorylated tau into NFTs and leads to synaptic dysfunction and neuronal loss (Selkoe, 1991; Mattson et al., 1998; Lue et al., 1999; Shankar and Walsh, 2009). These findings, together with the fact that select familial AD is due to causative gene mutations in the coding sequences of APP, PS1 and PS2, which are directly involved in Aβ formation, led to the development of the “Amyloid Cascade Hypothesis” first proposed by Selkoe in 1996 (Selkoe, 1996). The Amyloid Cascade Hypothesis states that abnormal processing of APP, whether genetic or spontaneous, causes overproduction of Aβ42 fragments. The brain is unable to clear the Aβ, and it gradually accumulates within the brain. The Aβ aggregates are toxic to neurons and synapses, and this leads to innate immune activation of microglia and astrocytes and attendant inflammatory processes, although the exact pathophysiology of this step is largely unknown (McGeer and McGeer, 2010). There is subsequent production and build-up of reactive intermediates and oxygen species that lead to oxidative injury (Kish et al., 1992; Gutowicz, 2011). Other changes in kinase and phosphatase activities lead to the formation of NFTs (Iqbal et al., 1998). These combined toxic effects are thought to be the underlying cause of neuronal loss, synaptic dysfunction, and the subsequent degradation of cognitive function in AD, and this is currently the most widely accepted hypothesis.

The Amyloid Cascade Hypothesis, however, cannot completely explain the degradation in AD based on several pieces of evidence (Struble et al., 2010). Firstly, therapeutics against Aβ, though successful in removing Aβ fragments in animal models and humans, do not stop the progression of AD, meaning it is not the single cause of dementia (Holmes et al., 2008; Salloway et al., 2009). Secondly, Aβ is normally found even in the healthy human brain and in normal cell culture of healthy humans and other mammals (Seubert et al., 1992; Shoji et al., 1992; Haass and De Strooper, 1999), and those individuals with APP or γ-secretase mutations that have higher deposits of Aβ do not show dementia until they are 20-40 years old (Bird, 2008). In fact, about 20-30% of individuals with normal cognition who never develop dementia show significant levels of amyloid burden in the brain (Bennett et al., 2006; Aizenstein et al., 2008). This has led research to aim for the discovery of the other underlying factor(s) that leads to the cognitive decline in AD.

3. Neuroinflammation

It has long been known that inflammation is a prominent factor, not just in AD, but in a number of age-associated diseases, including atherosclerosis, diabetes, rheumatoid arthritis, and multiple sclerosis (MS). In fact, data from transcriptome analyses of brain tissue from normally aging humans and mice showed significant up-regulation of certain genes that occurred with age, and about half of those genes were associated with inflammation and oxidative stress (Prolla, 2002; Lu et al., 2004; Loerch et al., 2008; Bishop et al., 2010). This can be partly corrected by caloric restriction (Lee et al., 2000), which suggests that energy expenditure-associated oxidative stress underlies the M1 to M2 conversion in the brain. The outcome of oxidative stress can be DNA damage and reduced repair (Haigis and Yankner, 2010). Highly conserved insulin/IGF-1, TOR, and sirtuin signaling pathways regulate these cellular responses, and the aging process potentially induces their dysregulation, leading to pro-inflammatory microglial activation.

Several pieces of evidence indicate the increased presence of inflammatory molecules in AD, including some of the following: the presence of elevated levels of pro-inflammatory cytokines like tumor necrosis factor-α (TNF-α) in the serum of AD patients (Fillit et al., 1991) and interleukin (IL) -6 in brain tissue (Bauer et al., 1991; Bauer et al., 1992) compared to age-matched controls; the increase of activated microglial cells surrounding senile plaques in the cerebral cortex (Haga et al., 1989); the localization of Aβ deposits to T cells, activated microglia, and reactive astrocytes in AD brains (Wegiel and Wisniewski, 1990; Wisniewski and Wegiel, 1991; Ishii and Haga, 1992); and of particular interest in this article is the down-regulated expression of a neuroimmune regulatory protein, cluster of differentiation 200 (CD200), seen in post-mortem brains of AD patients (Walker et al., 2009).

These findings provide evidence for the new hypothesis that the neuronal damage seen in AD is not simply due to the Aβ deposition or intracellular tau accumulation, but to the brain’s dysregulated inflammatory and oxidative responses to them (McGeer et al., 1989; McGeer et al., 1994). Indeed, some of the people with increased Aβ load did not develop AD, suggesting that a second factor is necessary for the disease development, such as microgliosis. Using two carbon 11 ([¹¹C])-labeled positron emission tomographic imaging agents, Pittsburgh Compound B (PIB, an Aβ marker) and PK-11195 (a benzodiazepine receptor ligand and a microglial marker), several groups examined if PIB and PK11195 binding correlated with cognitive function of AD patients. Two studies reported a negative correlation of cognitive function with microgliosis (PK-11195) but not with Aβ load (PIB) (Edison et al., 2008; Yokokura et al., 2011). However, another small-scale study showed no correlation of PK-11195 binding with clinical diagnosis (Wiley et al., 2009). Other studies showed that, although PIB binding was positively correlated with astrogliosis, it was largely independent from PK-11195 binding (Okello et al., 2009; Kadir et al., 2011). These studies suggest that Aβ load is directly correlated with astrogliosis but not with microgliosis, and therefore, microgliosis is potentially more associated with the cognitive decline in AD.

Of particular interest is the role of the microglial activation switch from the alternative to classical activation phenotype. This change in microglial activation skewing phenotype may be the cause of significant amounts of cortical and hippocampal atrophy that makes neurogenesis a prime target for studying ways to replenish that loss (Voloboueva and Giffard, 2011). This in turn draws attention to the increasing significance of the role of immunomodulatory molecules, such as CD200, in neuroimmune regulation. This review will go over the innate and adaptive immune systems of the brain and the role of classically or alternatively activated microglia, which lead into the possible mechanisms by which neurogenesis can be restored in those areas most affected by neuroinflammation. Lastly, this review will discuss the role of CD200 in immune homeostasis and how it may be used to reverse neuroinflammation based on neuroimmunological studies of AD mouse models and AD patients.

4. The Innate and Adaptive Immune Responses

The immune response of the central nervous system (CNS) follows a well-characterized signaling pathway in response to tissue injury, cell death, and pathogens (Carpentier and Palmer, 2009). The main cells that direct the innate immune response in the CNS are mononuclear phagocytes (brain resident microglia and perivascular macrophages). These cells function as sentinels in the brain and prominently express pathogen recognition receptors (PRRs) that recognize the signaling pathways activated or released by dying or damaged cells known as pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). PRRs include Toll-like receptors (TLRs), Nucleotide Oligomerization Domain (NOD)-like receptors (NLRs), and Retinoic acid-Inducible Gene 1 (RIG1)-like receptors (RLRs). TLRs are ubiquitously expressed and are often expressed on the plasma membranes of immune cells and glial cells, including microglia, astrocytes, and neurons (Bsibsi et al., 2002; Tang et al., 2007). Interestingly, Aβ itself also acts as a PAMP molecule and interacts with TLRs (Lotz et al., 2005; Reed-Geaghan et al., 2009). NLRs are found in the cytoplasm, and some NOD ligands, such as muramyl dipeptide (MDP), are also expressed in microglia, astrocytes, and neurons, and they have been shown to activate the neuronal cells in vitro (Chauhan et al., 2009; Li et al., 2010). When stimulated by a ligand, such as lipopolysaccharide (LPS, a TLR ligand) or MDP, TLRs or NLRs become activated and initiate transcriptional up-regulation and release of pro-inflammatory cytokines and chemokines (Bailey et al., 2006). This LPS-induced inflammatory response is enhanced in aged animals in vivo (Sparkman and Johnson, 2008; Lynch, 2010). The cytokines released include pro-inflammatory cytokines, such as TNF-α, interferon-γ (IFN-γ), IL-6, and IL-1β. The cytokines initiate the adaptive immune response, activate vasculature, and recruit circulating lymphocytes to the infection or injury. The lymphocytes bind to the activated endothelium and diffuse throughout the affected tissue. Through antigen recognition and with the help of B-cell-mediated antibody production, they remove pathogens by T-cell-mediated destruction of cells. Following the elimination of pathogens, the immune response self-limits itself by the clearance of the molecular patterns that initially triggered the immune response and by Fas ligand (FasL)-mediated apoptosis of activated T cells (Niederkorn, 2006). Both T and B cells undergo apoptosis. The hypothalamo-pituitary-adrenal (HPA) axis is activated during inflammation and increases the production of glucocorticoids to conversely decrease pro-inflammatory signaling. Perivascular macrophages are essential for the anti-inflammatory HPA response to IL-1 (Serrats et al., 2010). Without them, CNS responses to inflammatory insults are enhanced. These cells induce the HPA axis and constrain endothelial cell involvement in prostanoid production during the response to pro-inflammatory signals (Serrats et al., 2010). With the help of lymphocytes, microglia, and neurons, the HPA axis also contributes to the production of anti-inflammatory hormones and cytokines such as IL-4, IL-10, and transforming growth factor-β (TGF-β) (Elenkov and Chrousos, 2002). With that, the immune signaling cascade is resolved and returned to its previous state of surveillance of the CNS.

5. Classification of Macrophage Activation – M1/M2 Switch of activation phenotype

6. Classification of Microglial Activation

Microglia are members of the monocytic-macrophage family. Like macrophages, microglia adopt similar activation phenotypes in response to CNS insult. Table 1 characterizes microglial activation states M1, M2a, and M2c by denoting their specific ligands and markers and outlining their effects on neurogenesis, synaptic plasticity, and spatial learning based on in vitro and in vivo experiments, which will be discussed below. Our lab recently identified that resting microglia adopted the M2c phenotype (unpublished observation), showing high expression of TGF-β1, extracellular matrix-related genes, and expression of IL-10. Microglial activation phenotypes switch from M2 to M1 during disease progression. Increased expression of pro-inflammatory cytokines and chemokines is likewise accompanied by M1-skewed microglial activation in the human AD brain (Hoozemans et al., 2006). These findings were reproduced in some AD mouse models, such as the APP+PS1 mouse (Jankowsky et al., 2004), which demonstrated that there was a distinctive shift from M2 to M1 microglial activation in the hippocampus of aged rodents (Jimenez et al., 2008). It was found in aged rats, for example, that IFN-γ increased with age, while Il-4 decreased (Nolan et al., 2005; Maher et al., 2006), suggesting an increased classical activation phenotype over alternative with age. This microglial phenotype switch from M2c to M1 was also demonstrated by in vivo intracerebroventricular injection of LPS into mouse brains as determined by changes in the gene expression of M1 markers (CCL5, CCL11, CXCL9, CXCL10, CXCL11, CXCL16, CD16, IL-12, AND IL-23) and M2c markers (Scavenger Receptor A, Scavenger Receptor B, CD14, CCR2, fibrinogen, collagen, and SOCS3) (Kopydlowski et al., 1999; Mantovani et al., 2004; Lund et al., 2006; Qin et al., 2006; Baker et al., 2009; Villeda et al., 2011).

Further evidence from APP mice and human monocyte-derived macrophages and microglia showed that, while an increase in M1 activators, like IFN-γ receptor and TNF-R1, led to neuroinflammation and inhibited clearance of Aβ, an increase in M2a or M2c activators enhanced Aβ clearance (He et al., 2007; Yamamoto et al., 2007; Yamamoto et al., 2008). The same switch was seen in LPS-stimulated primary microglia (Lund et al., 2006). IL-10 is a modulator of M2c microglial activation (Mantovani et al., 2004). It was found that IL-10 mediated anti-inflammatory responses including decreasing glial activation and production of proinflammatory cytokines (Plunkett et al., 2001). With gene delivery of IL-10 into APP+PS1 mice, there was suppression of astro/microgliosis and a restoration of impaired spatial learning and neurogenesis, and while there was no reduction of β-amyloidosis, there was enhanced vascular transport of Aβ (Kiyota et al., 2011). IL-4, on the other hand, is an important modulator of M2a microglial activation (Mantovani et al., 2004). Gene delivery of IL-4 into APP+PS1 mice partially suppressed glial accumulation in the hippocampus, directly enhanced neurogenesis, restored impaired spatial learning, and also reduced Aβ deposition (Kiyota et al., 2010). Gene delivery of either IL-4 or IL-10 also reversed the LPS-induced deficit in LTP in the rat hippocampus (Lynch et al., 2004; Nolan et al., 2005). These findings and more are outlined in Table 2, which briefly summarizes the results of cytokine modulation on neuroinflammatory effects in AD mouse models. As can be seen in Table 2, APP mice encompass both M1 and M2c phenotypes, suggesting that the expression of the APP gene induces the up-regulation of both pro- and anti-inflammatory cytokines. Also note the contradictory findings in NOS2^−/− mice. It was previously found that APP mice crossed with a NOS2^−/− exhibited an M1 microglial activation phenotype and enhanced β-amyloidosis and impaired spatial/ contextual learning (Benzing et al., 1999; Apelt and Schliebs, 2001; Abbas et al., 2002). In the most recent publication, however, Kummer et al found that APP+PS1 mice crossed with NOS2^−/− had decreased β-amyloidosis and improved spatial/ contextual learning compared to APP+PS1 mice (Kummer et al., 2011). This may be due to their use of the APP+PS1 model instead of the APP-only mouse and differences in nitration of Aβ42. These findings demonstrate that there is an important role for M2a and M2c activators in microglial activation and clearance of Aβ in vivo. Although it is well established that there is a large immune response component to AD, it is still not completely understood which activation phenotype affects the onset or progression of the disease and which should be the therapeutic target. The failure of anti-inflammatory drugs, like non-steroidal anti-inflammatory drugs (NSAIDs), to halt the progression of AD drives the need for more research into the link between neuroinflammation and AD (Firuzi and Pratico, 2006; Martin et al., 2008; Cole and Frautschy, 2010). This could be due to the suppression of, not only pro-inflammatory, but also anti-inflammatory activation by endogenous molecules, inactivating the beneficial effect of M2a or M2c-skewed microglial functions. Microglial activation is a prominent feature of AD and serves as the driving force behind inflammation, and has recently been suggested to play a role in both neuroprotection and neurogenesis (Butovsky et al., 2006b; Zhao et al., 2006; Figueiredo et al., 2008; Shein et al., 2008; Xapelli et al., 2008; Gomi et al., 2011), making researchers question the exact nature of its association with AD pathology.

7. Neurogenesis

Adult neurogenesis is the process of neuronal proliferation and maturation in specific brain regions, and it is not only involved in CNS maintenance but also in the formation of new memories that new evidence suggests may be linked to synaptic plasticity affected by environmental or behavioral factors (Bruel-Jungerman et al., 2007; Deng et al., 2009; Deng et al., 2010; Tronel et al., 2010). Neural stem cells (NSCs) are always present in the adult CNS by self-renewal where they have the ability to differentiate into neurons, astrocytes, and oligodendrocytes. However, the process is strictly controlled by local signaling that keeps the production of new neurons to a very low level (Dayer et al., 2005; Tamura et al., 2007). The production of new neurons in adulthood is mainly seen in the subventricular zone (SVZ), where neuroblasts migrate to the olfactory bulb, and the subgranular zone (SGZ) of the hippocampal dentate gyrus, where the neurons are involved in hippocampal cell maintenance, the process of short-term memory formation and learning, and the regulation of mood (Ming and Song, 2005; Franklin and Ffrench-Constant, 2008). These areas have a network with high demand for the production of new neurons because they are constantly exposed to novel stimuli (Carpentier and Palmer, 2009). Only a small subset of NPCs created in the SGZ reach maturity after integration into the neural network, meaning a majority of the newborn cells undergo apoptosis within the first four days after creation (Sierra et al., 2010). It was shown by a number of researchers that exposure to novel smells in the olfactory bulb stimulated the production and retention of new neurons (Magavi et al., 2005; So et al., 2008), and it was likewise shown in the hippocampus that environmental enrichment, physical activity, and the act of learning promoted the production of new neurons (Emsley et al., 2005; Duan et al., 2008). Oddly enough, both depression and olfactory deficits are early indications of degeneration in both AD and Parkinson’s disease (PD) (Bruck et al., 2004; Hawkes, 2006; Gabryelewicz et al., 2007; Matsuda, 2007; Poewe, 2008). In fact, in co-cultures of resting or IL-4-activated microglia, NPCs expressing mutant PS1 variants have inhibited neuronal proliferation and differentiation compared to NPCs expressing wild-type human PS1, suggesting that there is a specific link between components causing AD and neurogenesis (Choi et al., 2008).

8. The Effect of Neuroinflammation on Neurogenesis

9. Neuron-Microglia Interaction through CD200 - CD200R Signaling

9.3 The Role of Dok in CD200R Signaling

One possible mechanism to look into is the Dok signaling pathway of the CD200R. As previously stated, when CD200 binds to CD200R, the interaction negatively modulates myeloid cells through binding of Dok2. While there has not been very much research on Dok proteins, they are involved in immune suppression signaling. They are expressed on peripheral T cells and most thymocytes, and act as negative regulators of T cell response signaling (Yasuda et al., 2007). Mice that lack Dok1 and/or Dok2 showed increased production of pro-inflammatory molecules, like TNF-α and NO, and enhanced responses to inflammation-inducing signals like those from IFN-γ and LPS (Shinohara et al., 2005; Yasuda et al., 2007).

These findings are similar to those seen in CD200^−/− mice whose lack of ligand or receptor expression correlated with increased neuroinflammation and tissue damage and production of TNF-α in response to LPS. There is no published study to date that shows the regulating relationship between CD200R and Dok proteins in microglia and inflammation. CD200R is believed to act via Dok2 signaling, but it is still unknown which is the controlling mechanism. Figure 1 outlines the unresolved pathways that CD200/CD200R/Dok2 signaling may follow to attain its anti-inflammatory effects. While Dok1/2^−/− mice seem to have similar phenotypes to CD200 or CD200R^−/− mice, it has not yet been tested whether treatment of Dok1/2^−/− mice with CD200 can compensate for maintenance of immune system homeostasis (Fig. 1).

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Fig. 1

Effects of CD200/CD200R/Dok2 Signaling

M2a-skewing anti-inflammatory cytokine IL-4, which initiates the inter-cellular interaction between CD200 on neurons and CD200R on microglia. CD200R signaling acts via Dok2 to contribute to the regulation of three distinct, but unsolved pathways that result in the following: 1) enhanced neurogenesis by up-regulation of differentiation of NPCs to assist in new memory formation; 2) inhibited release of pro-inflammatory molecules that initiate M1 microglial activation and subsequent neurodegeneration; and 3) enhanced synaptic plasticity, particularly LTP.

One important issue worthy of mentioning, however, is the limitations of using mouse models for modeling human diseases. Koning et al did a comparative study of CD200R expression in the mouse and the human, and found that, while induction of CD200R expression in human macrophages was dependent on IL-4, mouse CD200R expression was not (Koning et al., 2010). Induction of mouse CD200R expression was not discovered, but it was theorized that it involved site-specific combinations of cytokines (Snelgrove et al., 2008). Therefore, while the CD200R may be a suitable indicator of alternative activation in humans, this may be a species-specific mechanism. This shows that research into the controlling mechanism between CD200R and Dok signaling may be a key finding.

10. Concluding Remarks

Research has still been unable to pinpoint a single cause for the neuronal death and synaptic dysfunction that leads to the cognitive decline seen in AD. Increasing evidence suggests that the cause is an interaction between the down-regulation of important immune regulatory components, increased inflammation, and decreased neurogenesis, and it is becoming more widely believed that inflammation is the major culprit in this interaction. This neuroinflammation is orchestrated by M1-skewed microglial activation often associated with loss of neurons in specific brain regions due to the production of damaging reactive oxygen species and toxic intermediates (Gutteridge and Halliwell 1989; Pratico et al. 2001; Pratico and Sung 2004). Alternative M2 microglial activation, on the other hand, is important for resolution of this inflammation. M2 activation causes the release of anti-inflammatory cytokines like IL-4, which is important for M2a microglial activation, and IL-10, which is important for M2c.

There is a characteristic switch from M2 to M1 microglial activation in the brain that occurs both with age and with disease progression. There is an up-regulation of pro-inflammatory cytokines and a reduction in anti-inflammatory cytokines and proteins, like CD200 (Lyons et al., 2007b; Lyons et al., 2009; Walker et al., 2009). CD200 interacts with CD200R via the Dok signaling pathway to activate anti-inflammatory signals, and its age-dependent decline is associated with increased microglial activation (Lyons et al., 2007b). A lack of CD200 or Dok results in increased neuroinflammation in the brain (Shinohara et al., 2005; Yasuda et al., 2007). This is seen in both rodent and human brain tissue. Treatments with specific M2a or M2c anti-inflammatory cytokines, like IL-4 or IL-10, can improve deficits seen as a result of up-regulated inflammatory responses, and restore CD200 expression which is specific to IL-4 (Lyons et al., 2007b; Lyons et al., 2007a; Kiyota et al., 2010). Neuroinflammatory states have also been shown to greatly decrease neurogenesis in the SVZ and SGZ, which may explain the deficit in short-term memory formation in AD patients. Evidence suggests that M1 microglial activation suppresses, not the proliferation of NSCs, but the differentiation of NPCs (Waldau and Shetty, 2008). Treatment with M2 cytokines or proteins like CD200, IL-4, or IL-10, and growth factors like FGF-2 can possibly restore that deficit.

There is still no successful therapy available to halt and reverse the devastating neurodegeneration seen in AD, but the evidence leads one to conclude that the best treatment for AD may be a combined therapeutic with anti-inflammatory and pro-neurogenic components. While none have been thoroughly tested, IL-4, IL-10, CD200, FGF-2, and Dok signaling proteins have proven to be promising targets for further research. Regardless of the various potentials of these therapeutic targets, it is clear that there is still a lack of understanding of the complicated relationship between inflammation and AD that warrants further investigation.

Acknowledgement

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References